I see many ads that claim to make you a data scientist in 12 weeks. They say they can teach you Python programming, python libraries like Pandas, Matplotlib, and scikit-learn, another visualization tool like Tableau, SQL, and probably more. After 12 weeks, you will get a job that will earn you about 100,000 USD. But before that, you have to pay a good amount to tea those classes. How realistic they are.
Are They Realistic?
It depends on which level you are in. If you already know a programming language and switch to Python for a new career, then you can learn all this in three months if you work hard. But if you do not have any programming background, then it will be too ambitious to think that you can learn all this in three months even if you do not have another job and you focus only on study.
A Reasonable TimeFrame
If you want to become a data scientist, you need to learn at least one of these two languages. And learning programming languages does not mean just learning if/else statement and loops. It is more than that. You need to learn the data structures and programming problem-solving which takes some time. You should dedicate at least three months to learn a language only. If you do not and rush into learning libraries, databases all at once, you may end up learning everything to an extent that it will not be useful. I am not saying you need to be an absolute expert in programming before you can start learning anything else. But you need to be at least comfortable writing some code solving problems. There are a lot of programming problems out there to test yourself. I suggest, try leetcode. They have three different categories of problems in leetcode, easy, medium, and hard. See if you can solve some easy problems. Then move on to learn the libraries like Numpy, Pandas, Matplotlib, seaborn, scikit-learn, and others.
Learning just a few of those libraries should take another three months. It takes some time to practice and grasp the ideas of Exploratory Data Analysis and do it yourself. Learning SQL should not take too much time because you will find lots of similarities between Pandas and SQL. But still, even if you learn fast, learning to use several big datasets and intermediate level complex queries, organizing and setting up datasets will be a couple of months. So, I am talking about at least eight months.
These Are Just The Minimum
If you have good contacts and you are lucky enough, you will find a job after that. But you need to keep in mind that you have to keep learning more tools. More concepts. You need to keep improving your programming skills. One important thing is statistics. If you are already good at it, great! Otherwise, at least learn some beginner level inferential statistics and model fitting and learn to implement them in Python or R. Python’s scikit-learn library is just a tool for machine learning. But learning some genuine concepts will be useful. Also, I see Data Mining as an important skill. There is so much data out there. We need to extract them. Lots of job opening ask for it as well.
I do not want to be discouraging. If you can develop all those skills, you will be in demand in the job market. So, spending a year or two is not a bad idea at all. It will add so much value to your life.
What Is Reasonable In 12 Weeks To 18 Weeks
It looks too tough to become a data scientist in 12 weeks. But if you do not have that much time and want to get a job soon, probably becoming a Data Analyst will be a decent goal. If you are a college graduate or a college student, I am sure, you know excel.
- Polish your excel skills some more. Learn some advanced techniques like v-lookup, pivot table, Macros, visual basic. I think it will be faster to pick up for you. Excel is so advanced right now. There are a lot of data analyst roles that want advanced excel skills.
- Learn a good data visualization tool like Tableau. You can do quite a lot of visualization without writing any programming logic or any code. It has so many in-built options. Simple drag and drop can make complex visualizations.
- Learn SQL. Learning SQL can be easier than learning a programming language. SQL queries are like regular language. So it’s easier to grasp. Plus it is an invaluable skill in the job market. I meet so many people in different conferences who are working as SQL developers for the last 10 years.
- Start learning a programming language like Python or R. But you have to keep practicing it for a while to learn it well if this is your first language.
Develop Soft Skills
These three skills together should make you employable. But we focus too much on learning the tools but we forget to spend some time on developing soft skills.
- It is important to develop some business insights where you will use those tools. Without some good real-world knowledge, it will be hard to use those tools effectively. So, read articles, books, or newspapers to stay updated and develop some real-world knowledge. So, you can talk about how to use those tools in a crowd or an interview.
- Networking is another valuable skill. Attend meetups, go to seminars, conferences, listen to experienced people talk. That’s a good way to develop knowledge and also make contacts.
- Engage with the community in Stack Overflow, Stack Exchange, and Slack Channels. That will keep you updated about the job market, recent technologies, and improve your soft skills.
I am not against Bootcamp. I started my journey with a Bootcamp and I am grateful to that Bootcamp. But it was a six months long Bootcamp to learn programming concepts and SQL only which was realistic. We learned the basics of a few programming languages. More importantly, it was free. It was from LaunchCode. If you are in the US, please check. They are good. I am sure they are still free. My suggestion is, start taking free courses. It is even not necessary at all to pay for learning programming languages. There are a lot of great free courses out there. Try some of those free courses first. That will give you some insights. Probably, you will make better decisions about which boot camps to pay your or your parents’ hard-earned money.
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